2021年7月发射的风云三号E星(FY-3E)是世界首颗民用晨昏轨道气象卫星,其搭载的WindRAD双频测风雷达具有全球海面风场探测能力。本文首先基于FY-3E/WindRAD L1级观测资料,研究了雷达海面后向散射和风场之间的非线性关系,分别建立了适用于...2021年7月发射的风云三号E星(FY-3E)是世界首颗民用晨昏轨道气象卫星,其搭载的WindRAD双频测风雷达具有全球海面风场探测能力。本文首先基于FY-3E/WindRAD L1级观测资料,研究了雷达海面后向散射和风场之间的非线性关系,分别建立了适用于C和Ku波段VV/HH极化的地球物理模式函数(GMF)。随后,结合最大似然估计法(MLE)对WindRAD散射计探测资料进行风场反演。利用海洋浮标、中法海洋卫星散射计(CSCAT)和美国国家环境预报中心(NCEP)模式风场资料对WindRAD反演风场进行验证。结果显示:WindRAD反演风速与浮标风速偏差约为0.2 m s^(-1),均方根误差(RMSE)在1.13~1.44 m s^(-1)之间,优于2 m s^(-1)的业务化应用的风速精度要求;两者风向偏差在1.4°~3.0°之间,RMSE在25.3°~30.1°之间。WindRAD和CSCAT风场具有较好的一致性,风速RMSE在1.37~1.6 m s^(-1)之间,风向RMSE在22.9°~25.9°之间。WindRAD和NCEP模式风速RMSE在1.87~2.23 m s^(-1)之间,风向RMSE在22.4°~27.1°之间。研究表明WindRAD散射计C和Ku波段VV/HH极化反演风场均具有较高的精度,充分显示了WindRAD载荷在全球海面风场探测方面的应用潜力和价值。展开更多
多源降水融合技术是精准估算降水时空分布的重要手段,但常规融合方法难以充分考虑降水的空间局部相关性和时间依赖性以再现降水的空间分布格局。该研究选择3套卫星降水产品(IMERG,CMORPH和GSMaP)和站点观测数据,构建三维卷积神经网络(th...多源降水融合技术是精准估算降水时空分布的重要手段,但常规融合方法难以充分考虑降水的空间局部相关性和时间依赖性以再现降水的空间分布格局。该研究选择3套卫星降水产品(IMERG,CMORPH和GSMaP)和站点观测数据,构建三维卷积神经网络(three-dimensional convolutional neural network,3DCNN)和卷积长短期记忆神经网络(convolution long short term memory neural network,ConvLSTM)集成的时空深度学习融合模型(3DCNN-ConvLSTM),通过深度挖掘降水的时空变化特征,实现降水数据的精确估计。结果表明,在日尺度上,3DCNN-ConvLSTM融合降水的性能显著优于原始卫星降水产品,融合后的相关系数和克林-古普塔效率系数分别提高至0.679和0.64,均方根误差较融合前降低11.7%~24.4%,平均绝对误差降幅为9.3%~20.7%,且针对不同强度降水事件的捕捉精度更高;在月尺度上,各月降水性能得到不同程度的改善,其中高降水月份提升更显著;在空间尺度上,融合模型校正了原始降水产品在空间上的高估现象,在不同地形上表现出最高相关性及最小误差。与其他融合模型相比,3DCNN-ConvLSTM在提升降水数据精度方面表现更出色。总之,考虑了降水时空相关性的多源降水融合模型,能够有效提升闽浙赣地区降水数据质量,在多源降水融合领域有一定应用价值。展开更多
The calculation of viewing and solar geometry angles is a critical first step in retrieving atmospheric and surface variables from geostationary satellite observations.Whereas the viewing angles for geostationary sate...The calculation of viewing and solar geometry angles is a critical first step in retrieving atmospheric and surface variables from geostationary satellite observations.Whereas the viewing angles for geostationary satellites are not timevarying,a primary source of inaccuracy in solar positioning is the use of a single timestamp.Since pixel scanning times can differ significantly across the field-of-view disk(e.g.,by approximately 13 min for Fengyun-4B),this practice leads to errors of up to±2°in solar zenith angle,which translates to±50 W m^(−2) in extraterrestrial irradiance;the errors in solar azimuth angle can exceed±100°.Beyond scanning time,this work also quantifies the impact of other inputs—including altitude,surface pressure,air temperature,difference between Terrestrial Time and Universal Time,and atmospheric refraction—on the resulting angles.A comparison of our precise calculations with the official National Satellite Meteorological Center L1_GEO product shows an accuracy within 0.1°,confirming its utility for most retrieval tasks.To facilitate higher precision when required,this work releases the corresponding satellite and solar positioning codes in both R and Python.展开更多
Accurate precipitation estimation in semiarid,topographically complicated areas is critical for water resource management and climate risk monitoring.This work provides a detailed,multi-scale evaluation of four major ...Accurate precipitation estimation in semiarid,topographically complicated areas is critical for water resource management and climate risk monitoring.This work provides a detailed,multi-scale evaluation of four major satellite precipitation products(CHIRPS,PERSIANN-CDR,IMERG-F v07,and GSMaP)over Isfahan province,Iran,over a 9-year period(2015-2023).The performance of these products was benchmarked against a dense network of 98 rain gauges using a suite of continuous and categorical statistical metrics,following a two-stage quality control protocol to remove outliers and false alarms.The results revealed that the performance of all products improves with temporal aggregation.At the daily level,GSMaP performed marginally better,although all products were linked with considerable uncertainty.At the monthly and annual levels,the GPM-era products(IMERG and GSMaP)clearly beat the other two,establishing themselves as dependable tools for long-term hydro-climatological studies.Error analysis revealed that topography is the dominant regulating factor,creating a systematic elevationdependent bias,largely characterized by underestimation from most products in high-elevation areas,though the PERSIANN-CDR product exhibited a contrasting overestimation tendency.Finally,the findings highlight the importance of implementing local,elevation-dependent calibration before deploying these products in hydrological modeling.展开更多
Rainfall input errors are a major source of uncertainty in flood forecasting,and merging multi-source precipitation data is essential for improving accuracy.Traditional merging methods often prioritize precipitation m...Rainfall input errors are a major source of uncertainty in flood forecasting,and merging multi-source precipitation data is essential for improving accuracy.Traditional merging methods often prioritize precipitation magnitude enhancements while overlooking event detection and false alarms.To address these limitations,this study developed a precipitation integration framework that combines machine learning classification-plus-regression models with Bayesian model averaging(BMA).Three machine learning algorithms-categorical boosting(CatBoost),light gradient boosting machine(LightGBM),and random forest(RF)-were used to improve precipitation event detection.The framework includes spatial unification of raw satellite products using bilinear interpolation,bias correction through classification-plus-regression models,and final merging via a seasonal-scale BMA model.The method integrated GSMaP,IMERG,and PERSIANN satellite precipitation products,with ground observations used for model training(2001-2014)and independent validation(2015-2020)in the Upper Ganjiang River Basin,China.Results showed that the framework significantly enhanced precipitation estimation accuracy and detection capability.LightGBM-based integration exhibited superior detection performance(FAR=0.08,CSI=0.86),while RF-based integration achieved the highest overall accuracy(RMSE=4.67,CC=0.92).Seasonal variations in BMA weights underscored the need to account for seasonal characteristics of precipitation products.Additionally,accuracy improvements were observed across all rainfall categories,especially for heavy rainstorms.The seasonal-scale BMA fusion has combined the strengths of individual corrections and further enhanced precipitation estimation.This research offers a robust method for generating accurate rainfall inputs,providing valuable support for hydrological modeling and flood forecasting applications.展开更多
利用中国区域2023年夏季945个地基全球导航卫星系统(GNSS)测站的观测数据,分别采用双差网解法与精密单点定位法(Precise Point Positioning,PPP)对大气可降水量(Precipitable Water Vapor,PWV)进行了反演,以同址探空站和ERA5再分析资料...利用中国区域2023年夏季945个地基全球导航卫星系统(GNSS)测站的观测数据,分别采用双差网解法与精密单点定位法(Precise Point Positioning,PPP)对大气可降水量(Precipitable Water Vapor,PWV)进行了反演,以同址探空站和ERA5再分析资料的PWV为参考值,研究分析了两种方法在中国不同气候区域反演PWV的精度及稳定性特征。结果表明:与PPP解相比,双差解与探空和ERA5资料的PWV的相关性更强,偏差(Bias)频率分布更集中,峰值区概率更高,偏差范围更小。以探空资料获取的RS-PWV为参考值时,双差解与PPP解的平均Bias分别为-0.1 mm和1.1 mm,平均均方根误差(RMSE)分别为2.4 mm和3.1 mm,以ERA5-PWV为参考值时,双差解与PPP解的平均Bias分别为-0.2 mm和0.1 mm,平均RMSE分别为2.7 mm和3.2 mm,双差解的平均RMSE均小于3 mm,这表明双差网解法反演的PWV具有更高的精度和稳定性。GNSS探测水汽的精度总体表现为西部非季风区优于东部季风区,双差解在各气候区域的RMSE都更集中于中位数附近,而PPP解在不同测站多表现出不同的精度水平,在水汽充足且探测精度偏低的温带和亚热带季风气候区域精度离散程度较大,具有较强的不稳定性。展开更多
文摘2021年7月发射的风云三号E星(FY-3E)是世界首颗民用晨昏轨道气象卫星,其搭载的WindRAD双频测风雷达具有全球海面风场探测能力。本文首先基于FY-3E/WindRAD L1级观测资料,研究了雷达海面后向散射和风场之间的非线性关系,分别建立了适用于C和Ku波段VV/HH极化的地球物理模式函数(GMF)。随后,结合最大似然估计法(MLE)对WindRAD散射计探测资料进行风场反演。利用海洋浮标、中法海洋卫星散射计(CSCAT)和美国国家环境预报中心(NCEP)模式风场资料对WindRAD反演风场进行验证。结果显示:WindRAD反演风速与浮标风速偏差约为0.2 m s^(-1),均方根误差(RMSE)在1.13~1.44 m s^(-1)之间,优于2 m s^(-1)的业务化应用的风速精度要求;两者风向偏差在1.4°~3.0°之间,RMSE在25.3°~30.1°之间。WindRAD和CSCAT风场具有较好的一致性,风速RMSE在1.37~1.6 m s^(-1)之间,风向RMSE在22.9°~25.9°之间。WindRAD和NCEP模式风速RMSE在1.87~2.23 m s^(-1)之间,风向RMSE在22.4°~27.1°之间。研究表明WindRAD散射计C和Ku波段VV/HH极化反演风场均具有较高的精度,充分显示了WindRAD载荷在全球海面风场探测方面的应用潜力和价值。
文摘多源降水融合技术是精准估算降水时空分布的重要手段,但常规融合方法难以充分考虑降水的空间局部相关性和时间依赖性以再现降水的空间分布格局。该研究选择3套卫星降水产品(IMERG,CMORPH和GSMaP)和站点观测数据,构建三维卷积神经网络(three-dimensional convolutional neural network,3DCNN)和卷积长短期记忆神经网络(convolution long short term memory neural network,ConvLSTM)集成的时空深度学习融合模型(3DCNN-ConvLSTM),通过深度挖掘降水的时空变化特征,实现降水数据的精确估计。结果表明,在日尺度上,3DCNN-ConvLSTM融合降水的性能显著优于原始卫星降水产品,融合后的相关系数和克林-古普塔效率系数分别提高至0.679和0.64,均方根误差较融合前降低11.7%~24.4%,平均绝对误差降幅为9.3%~20.7%,且针对不同强度降水事件的捕捉精度更高;在月尺度上,各月降水性能得到不同程度的改善,其中高降水月份提升更显著;在空间尺度上,融合模型校正了原始降水产品在空间上的高估现象,在不同地形上表现出最高相关性及最小误差。与其他融合模型相比,3DCNN-ConvLSTM在提升降水数据精度方面表现更出色。总之,考虑了降水时空相关性的多源降水融合模型,能够有效提升闽浙赣地区降水数据质量,在多源降水融合领域有一定应用价值。
基金supported by the National Natural Science Foundation of China(Grant No.42375192).
文摘The calculation of viewing and solar geometry angles is a critical first step in retrieving atmospheric and surface variables from geostationary satellite observations.Whereas the viewing angles for geostationary satellites are not timevarying,a primary source of inaccuracy in solar positioning is the use of a single timestamp.Since pixel scanning times can differ significantly across the field-of-view disk(e.g.,by approximately 13 min for Fengyun-4B),this practice leads to errors of up to±2°in solar zenith angle,which translates to±50 W m^(−2) in extraterrestrial irradiance;the errors in solar azimuth angle can exceed±100°.Beyond scanning time,this work also quantifies the impact of other inputs—including altitude,surface pressure,air temperature,difference between Terrestrial Time and Universal Time,and atmospheric refraction—on the resulting angles.A comparison of our precise calculations with the official National Satellite Meteorological Center L1_GEO product shows an accuracy within 0.1°,confirming its utility for most retrieval tasks.To facilitate higher precision when required,this work releases the corresponding satellite and solar positioning codes in both R and Python.
文摘Accurate precipitation estimation in semiarid,topographically complicated areas is critical for water resource management and climate risk monitoring.This work provides a detailed,multi-scale evaluation of four major satellite precipitation products(CHIRPS,PERSIANN-CDR,IMERG-F v07,and GSMaP)over Isfahan province,Iran,over a 9-year period(2015-2023).The performance of these products was benchmarked against a dense network of 98 rain gauges using a suite of continuous and categorical statistical metrics,following a two-stage quality control protocol to remove outliers and false alarms.The results revealed that the performance of all products improves with temporal aggregation.At the daily level,GSMaP performed marginally better,although all products were linked with considerable uncertainty.At the monthly and annual levels,the GPM-era products(IMERG and GSMaP)clearly beat the other two,establishing themselves as dependable tools for long-term hydro-climatological studies.Error analysis revealed that topography is the dominant regulating factor,creating a systematic elevationdependent bias,largely characterized by underestimation from most products in high-elevation areas,though the PERSIANN-CDR product exhibited a contrasting overestimation tendency.Finally,the findings highlight the importance of implementing local,elevation-dependent calibration before deploying these products in hydrological modeling.
基金supported by the National Natural Science Foundation of China(42471049).
文摘Rainfall input errors are a major source of uncertainty in flood forecasting,and merging multi-source precipitation data is essential for improving accuracy.Traditional merging methods often prioritize precipitation magnitude enhancements while overlooking event detection and false alarms.To address these limitations,this study developed a precipitation integration framework that combines machine learning classification-plus-regression models with Bayesian model averaging(BMA).Three machine learning algorithms-categorical boosting(CatBoost),light gradient boosting machine(LightGBM),and random forest(RF)-were used to improve precipitation event detection.The framework includes spatial unification of raw satellite products using bilinear interpolation,bias correction through classification-plus-regression models,and final merging via a seasonal-scale BMA model.The method integrated GSMaP,IMERG,and PERSIANN satellite precipitation products,with ground observations used for model training(2001-2014)and independent validation(2015-2020)in the Upper Ganjiang River Basin,China.Results showed that the framework significantly enhanced precipitation estimation accuracy and detection capability.LightGBM-based integration exhibited superior detection performance(FAR=0.08,CSI=0.86),while RF-based integration achieved the highest overall accuracy(RMSE=4.67,CC=0.92).Seasonal variations in BMA weights underscored the need to account for seasonal characteristics of precipitation products.Additionally,accuracy improvements were observed across all rainfall categories,especially for heavy rainstorms.The seasonal-scale BMA fusion has combined the strengths of individual corrections and further enhanced precipitation estimation.This research offers a robust method for generating accurate rainfall inputs,providing valuable support for hydrological modeling and flood forecasting applications.
